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Z-Platform is the new innovative powerful and complex platform to ingest data of any kind and store the data in the form of JSON documents in MongoDB and represent a sparse representation of the same in Neo4j graph database. Mahesh discusses how he tackled deadlocks and improved the performance of the system significantly. The test environment included small graphs (ranging up to 10000 relationships to very large graphs (ranging up to 39 million relationships). The average performance of the system is 3741 relationships per minute.
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Avoiding Deadlocks in Neo4j on Z-Platform
- Mahesh Chaudhari, Cesar Arevalo & Brian Roy
2
Outline
• Introduction to the Z-Platform• Problems caused by Deadlocks• Locks and Deadlocks in Neo4j• Avoidance using Bipartite graphs• Performance• Conclusion
3
Z-PlatformMongoDB Database
Z-Platform
Neo4j Graph Database
Json Documents Nodes & Edges
Source Datasets
Full Entity Profiles
Sparse Representation of Profiles
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Deadlocks in Z-Platform
• Creating relationships is one of the most time consuming processes
• Log analysis reveals deadlocks among batch transactions and retry-mechanism takes time
• Dependent on how nodes and relationships are grouped together
• Batch size is dependent on the size of the JSON block sent to the server
• Time required to build relationships and resolve deadlocks is in the order of seconds
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Locks in Neo4j
• Create a Node n1 Write Lock on Node n1• Update a Node n1 Write Lock on Node n1
but read available on Node n1• Create a Relationship r1 between nodes n1
and n2 Write Locks on relationship r1, n1 and n2
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Deadlocks across processes• Processes: P1 and P2• Nodes: A, B, C, D• Relationships: R1, R2, R3, R4
A B
C D
P1
P2
No Deadlocks
R1
R2
A B
A D
P1
P2
Possibility of Deadlock
R1
R3
A
C
B
D
P1
P2
No Deadlocks A B
A D
P1
P2
Deadlock
R1
R3
A CR4
C DR2
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Deadlocks across Transactions
• Transactions are also like separate processes but in a single thread or multiple threads
• Deadlocks occur across transactions– Two concurrent transactions need write locks on the
same node n1– In two concurrent transactions, T1 has write lock on
node n1 and waiting on write lock on node n2 whereas T2 has write lock on n2 and is waiting for write lock on node n1
– Transactions of varying sizes
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Concurrent Transactions Deadlocks
A B
C D
T1
T2
No Deadlocks
A B
A D
T1
T2
Possibility of Deadlock
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Sequential Asynchronous Transactions Deadlocks
A B
C D
T1
T2
No Deadlocks
E F
.
.
.n edges
A D T2
Deadlock
A B
T1
E F
.
.
.n edges
A D T1
No Deadlock
A B
T2
E F...
n edges
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Deadlocks Detection and Avoidance
• Deadlocks Detection– Only possible at run-time– Recovery from deadlock is either to abort or retry
• Deadlocks Avoidance– Reorder the operations to lower or eliminate the
likelihood of deadlocks• Graph Clustering Algorithms: Most of them require
knowledge of entire graph
Clustering Relationships Bipartite Graphs
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Bipartite Graphs• Given a Graph G with Vertices V and Edges E,
then graph G is a bipartite graph such that vertices V can be partitioned into two independent sets V1 and V2.
A E
CD
B
A
C
B
D
E
V1 V2
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Creating Bipartite Graphs• Use two colors to color each node such that
no two adjacent nodes have the same color.
A E
CD
B
A
C
B
D
E
V1 V21 2
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Non-Bipartite Graphs
A E
CD
B
A
C
B
D
E
V1 V2
1 2
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Algorithm to generate Graph
• Create all the nodes• Create batches of
relationships among the same colored nodes
• Create batches of relationships across the two colors
A
C
B
D
E
V1 V2
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Algorithm in Z-Platform
• Batch of relationships R = {r1, r2, r3….. rn} :– each r is a triplet {src, dest, props} where src and dest are
nodes and props is a set of key-value pairs• Color the nodes based on each relationship with two
colors• Mark the conflicting edges where both the src and
dest nodes are of the same color• Batch these relationships together in a single batch• Start grouping the remaining edges such that no two
batches have any node in common
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Performance – Test Setup
• JDK 1.7• Neo4j Java Binding Rest API• Neo4j Enterprise Server 1.9• Batch size (configurable) : 2000• Test Program that generates random nodes
(max 1000) and relationships (max 10,000)• Huge file that contains 10,226 nodes and
39,564,960 relationships (5 GB)
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Performance – Creating Nodes
• 10,226 Nodes: 5.07 seconds• Average Time for 2000 Nodes: 0.99 seconds ~ 1 second• Each Node has 11 properties
1 2 3 4 5 60
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Time in seconds for Nodes
Time in Secs
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Performance – Creating Relationships
• 1,74,000 Relationships created in 47.16 seconds• Average Time for 2000 relationships: 0.54 seconds• Number of relationships per second: 3,689
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 850
0.2
0.4
0.6
0.8
1
1.2
1.4
Time in Seconds for relationships
Time in Secs
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Performance – Creating 39 Million Relationships
• 39,564,960 Relationships in : 10,573.56 seconds (2 hrs 56 mins 13 seconds)• Average Time for 2000 relationships: 0.53 seconds• Number of relationships per second: 3,741
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Graph Visualized in the Neo4j 2.0
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Future Work
• Test performance over the network using Amazon EC2 servers to mimic real world setup
• Single threaded application multi-threaded to see if better performance– More complex algorithm to batch relationships together– Analyze if the complexity is worth the performance improvement
• Vary multiple factors:– Batch size : 1000 to 4000– Properties (relationship descriptors) : 2 – 20
• Dispatcher Pattern to facilitate the single point distribution of nodes and relationships to threads/Transactions
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Conclusion
• Deadlocks in general are time consuming and difficult to detect and prevent
• Use of graph coloring to partition graph into conflicting and non-conflicting edges
• Successful prototype tests shows significant improvement in building relationships varying from small number to a very large number
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Contact Information
Zephyr Health Inc.
589 Howard St. 3rd Flr. San Francisco, California 94105+1.415.529.7649
zephyrhealthinc.com
Sven JunkergårdDirector of Technology+1 415 503 [email protected]
Brian RoyDirector of Platform Engineering & Architect+1 415 663 [email protected]
